它让不同环境的多个智能体各自进化再合并，本地数据不用集中也能互相借鉴，异质任务下提升最明显。
EvolveNet 提出一种协作式 harness 进化框架，把经验提取移到数据端，共享 harness 分发给各数据本地代理各自进化。每个代理只回传程序改编，再由作用域类型和证据引导的程序聚合合并成新共享 harness。在 text-to-SQL、数据科学编码、竞赛编程、软件工程和智能体工作流五项设置中全部获得提升，异质工作负载下收益最大。论文编号为 arXiv:2608.04968v1。
EvolveNet: Collaborative Harness Evolution for Agent Self-Improvement
The capabilities of an LLM agent depend not only on its model but on the harness: the executable program that constructs context, invokes tools, verifies results, and recovers from failure. Recent work shows that evolving the harness yields persistent improvements without updating model weights. Existing approaches, however, assume that all execution experience can be routed to a single optimizer, which evolves one harness along a sequential trajectory. Real agent ecosystems violate that assumption: users, organizations, and environments generate isolated streams of experience that cannot be pooled, so the experience most worth learning from is exactly the experience that cannot be directly centralized. We introduce EvolveNet, a paradigm of collaborative harness evolution that moves experience extraction to the data. A shared harness is broadcast to data-local agent deployments, each of which evolves it on its own workload. Only the resulting program adaptations are composed into an updated shared harness and redistributed, so that every participating agent inherits operational experience discovered by the others. By shifting the aggregation boundary from raw workloads to learned adaptations, EvolveNet keeps workloads local and allows multiple evolutionary searches to proceed concurrently with reduced serial depth. Because independently modified programs cannot be averaged like model parameters and may conflict when composed, EvolveNet introduces scope-typed, evidence-guided program aggregation. Across five settings spanning text-to-SQL, data-science coding, competitive programming, software engineering, and agentic workflows, EvolveNet improves the shared harness in all five, with the largest gains under heterogeneous workloads, and ablations attribute the improvement to composition of adaptations from different agents rather than to selecting among them.